Feeling Sleepy? Stop Driving—awareness Of Fall Asleep Crashes Part 1

Abstract

Study Objectives: To examine whether drivers are aware of sleepiness and associated symptoms, and how subjective reports predict driving impairment and physiological drowsiness. 

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Methods: Sixteen shift workers (19–65 years; 9 women) drove an instrumented vehicle for 2 hours on a closed-loop track after a night of sleep and a night of work. Subjective sleepiness/symptoms were rated every 15 minutes. Severe and moderate driving impairment was defined by emergency brake maneuvers and lane deviations, respectively. Physiological drowsiness was defined by eye closures  (Johns's drowsiness scores) and EEG-based microsleep events. 

Results: All subjective ratings increased post-night shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76–2.4, AUC > 0.81, p < 0.009), except “head dropping down”. Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to the center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17–1.24, p<0.029), although accuracy was only “fair” (AUC 0.59–0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30–2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC>0.62). KSS, likelihood of falling asleep, ocular symptoms, and“nodding off” predicted microsleep events, with fair-to-good accuracy (AUC 0.65–0.73). 

Conclusions: Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. Drivers should self-assess a wide range of sleepiness symptoms and stop driving when these occur to reduce the escalating risk of road crashes due to drowsiness.

Key words: Drowsiness; sleepiness; subjective; driving; lane departure; eye closure

Graphical Abstract

Statement of Significance

Drowsy driving remains a significant public health concern, contributing to up to 20% of serious injury/fatal crashes. Public education messaging to reduce the risk of falling asleep at the wheel recommends that drivers stop and take a break when drowsy. In a prospective, on-road study, we show drivers are aware of sleepiness while driving. As drivers may not routinely ask “How sleepy do I feel,” we recommend that they should also reflect on sleepiness symptoms, particularly those relating to the eyes or driving behavior (e.g. ‘struggling to keep the eyes open and/or the center of the road). Ignoring these earlier warning signs, or waiting for symptoms such as head nodding to develop, represents a significant risk to road safety.

Introduction

Drowsy driving remains a significant public health concern. In the United States, drowsiness is involved in 7% of all motor vehicle crashes, and 13%–21% of those resulting in serious injury or fatality [1]. Due to the difficulty in determining causality in drowsiness-related crashes [2], these figures likely underestimate the true extent of the problem, which is estimated to be 40% of all highway crashes [3]. Despite these risks however, driving while drowsy remains common, such that 1 in 25 drivers report having fallen asleep while driving in the past month [4]; the equivalent of ~1.16 million US drivers falling asleep at the wheel each month [5]. Since drowsiness is the result of insufficient sleep, prolonged wakefulness, and/or driving during the nighttime hours, these crashes are largely preventable. However, many individuals are routinely exposed to drowsiness-inducing factors that are beyond their control. This is particularly evident for night-shift workers, who are at increased risk of drowsy driving, particularly on the commute home following an overnight shift [6–10]. As drowsiness is an inevitable consequence for a large majority of these workers, strategies beyond simply “avoiding drowsiness” are required.

Road safety is a shared responsibility, and drivers should ensure they are safe to drive. From a drowsiness perspective, drivers can use simple scales, such as the Karolinska Sleepiness Scale  (KSS), to routinely assess how sleepy they feel before driving and throughout the drive. In a study of healthcare workers driving home after an extended duration of work shift, a pre-drive KSS assessment greater than six (“Some signs of sleepiness”) predicted 91% of subsequent drives with an adverse driving event  [6]. During a drive, however, previous research has suggested that some individuals are poor at assessing sleepiness and/or predicting an impending involuntary sleep episode [11–13].  Contrary to this, our recent systematic review and meta-analysis  (ma) suggested that drivers are aware of sleepiness, such that KSS while driving was correlated with both ocular (rma = 0.70) and brain (rma = 0.74) derived measures of drowsiness, and predicted subsequent lane deviations and crash risk [14]. Despite this, the majority of studies captured in the review utilized simulated driving/  laboratory environments, highlighting a need for an examination of subjective sleepiness and objective drowsiness and associated driving outcomes in real-world environments. Moreover, as many drivers reporting sleepiness continue to drive [15], it has been suggested that drivers may not recognize sleepiness as serious or specific enough to cease driving [16]. For instance, using a retrospective questionnaire approach, Nordbakke, and Sagberg reported that drivers often notice specific symptoms such as “difficulty keeping the eyes open” or “difficulty remaining in the center of the road” before falling asleep when driving [16], while a prospective low fidelity simulator study reported that sleepiness symptoms (such as “blurred vision”) were related to both subjective sleepiness (KSS) and driving impairment [17]. While assessing these symptoms may better assist drivers to recognize their level of sleepiness and ability to drive safely, this has not yet been examined prospectively and under real-world conditions.

To specifically address these two gaps in knowledge, we conducted new analyses of our earlier perspective, an on-road track study in night-shift workers [18] to determine the extent to which drivers are aware of sleepiness, and how this predicts subsequent physiological drowsiness and unsafe driving outcomes while driving. Specifically, we will address which sleepiness symptoms best predict adverse driving outcomes by examining self-reported sleepiness and provide optimal thresholds of those measures by which drivers should take corrective action.

Methods 

Participants

Sixteen night-shift workers (nine women) between the ages of 19   and 65 years (M = 48.7 ± 14.8 years) took part in the study. They worked regular night shifts (at least 5 continuous hours between  22:00–08:00, M = 3.1 shifts/week) across a variety of shift work sectors, held a valid United States or International driver’s license for >2 years (M=27.4 years ±16.5 years), and had normal visual acuity (with or without corrective lenses). Participants provided full informed consent and were remunerated for their time. Ethical approval was obtained from the Brigham and Women’s  Institutional Review Board (#2011P000370) and Monash University  Human Research Ethics Committee (#25777).

Design and protocol

A within-subject, cross-over design was used as previously described [18]. Participants drove a dual-control, instrumented vehicle around a closed-loop track for 2 hours on two occasions: after a night of work (at least 5 hours of work between 10:00 pm–08:00 am) and a night of sleep (at least 5 hours of sleep between 10:00 pm–08:00 am). Due to the ecological nature of the study, no manipulation of the sleep/wake schedule was implemented and drive order was not counterbalanced. A post hoc check, however, confirmed no effect of drive order on study outcomes (see  [18] for details). Sleep/wake timing, medication use, and caffeine/  alcohol use were monitored throughout the study using diaries for 1 week before each driving session. Participants were transported to and from the test facility via taxi for safety, and accompanied by a researcher to ensure they remained awake and did not consume caffeine in the two hours before the drive. Drives were initiated 2 hours post-nightshift which corresponded to between 09:30 am and 02:30 pm. The post-sleep and post-nightshift drives were time-matched to control for any time-of-day effects between drives. Drivers were asked to adhere to typical driving conditions (e.g. within the road markings, within speed limit requirements), and stopped briefly (<2 minutes) every 15   minutes to conduct sleepiness assessments.

Measurements

Subjective ratings of sleepiness and sleepiness symptoms were monitored at 15-minute intervals throughout the drive, alongside continuous assessment of driving impairment and physiological drowsiness (See Table 1).

1. KSS: Participants rated how sleepy they felt in the past 5 minutes on a 9-point scale from “extremely alert” to “extremely sleepy” [19], using the adapted KSS with descriptors on each point [13]. Higher scores indicate higher sleepiness.

2. Likelihood of falling asleep (LFA): Participants rated how likely they might fall asleep in the next 5 minutes on a 5-point scale of “very unlikely” to “very likely” [20], with higher scores indicating higher likelihood (scale reversed after data collection).

3. Sleepiness symptoms questionnaire (SSQ) - Participants rated the frequency of eight sleepiness symptoms on a 7-point   scale, ranging from “not at all” to “most of the time” [17].  Higher scores indicated a greater frequency of the sleepiness symptom. The eight sleepiness symptoms (SSQ1–8)   can be seen in Table 1 and include ratings related to drowsiness (e.g. struggle to keep eyes open), attention (e.g. mind wandering), and driving performance (e.g. difficulty keeping to the middle of the road).

4. Adverse driving events: Participants drove a 2002 Ford  Windstar minivan (Ford Motor Company) around a closed loop driving track (0.8 KM) for 2 hours. The vehicle was equipped with a dual brake and forward-facing cameras for verification of driving events. A safety observer (W.J.H.   or Y.L.), blinded to the condition, accompanied the driver in the front passenger seat, and initiated emergency braking procedures if the driver entered a “near-crash” situation.  Events, where the vehicle deviated from the lane, were recorded using a forward-facing camera and later verified post-drive by independent assessors blind to condition.  These events were respectively categorized as severe (near-crash/emergency braking) and moderate (lane deviation)   driving events (See Table 1).

5. Physiological drowsiness events: Eye and eyelid movements were monitored continuously using infrared reflectance oculography (Optalert, Melbourne, Australia). An  IR-transducer attached to an open-lens glasses frame emits and detects an IR light providing an accurate measure of the opening and closing of the eyelid [21–23] for each blink/eye closure. The Johns drowsiness score (JDS) is a score between 1 and 10 calculated using a proprietary algorithm based on several eyelid movements that are sensitive to sleepiness and is generated every minute throughout the drive. A level of 4.5 is associated with driving “off-road” in a car simulator [22], while a level of 2.6 has been associated with an increased risk of an attentional lapse  [24] or an out-of-lane driving event [25]. We examined the number of events where the JDS exceeded these established thresholds and respectively categorized as “severe” and “moderate” drowsiness events (See Table 1).

Electroencephalography (EEG) was continuously monitored during the drive (Vitaport 4, Temec), with electrodes placed down the midline at frontal, central, parietal, and occipital positions (Fz,  Cz, Pz, and Oz, respectively). Data were scored for EEG-derived microsleeps defined as activity <8Hz for at least 3 seconds, in any electrode site. These were identified as ‘end state’ fall-asleep events (Table 1).

Data analysis

To describe the effect of the condition on the outcome variables,   we used Fishers Exact (emergency braking) or Poisson regression (lane deviation, JDS ≥2.7 and 4.5+ scores, and Microsleep events). Subjective sleepiness ratings were obtained in 15-minute bins. SSQ   was analyzed as both a global score and for each item individually. To examine the effect of shift and drive duration on subjective ratings,   linear mixed-effects models were used, with condition (post-sleep vs. post-shift) and drive duration (8 × 15-minute bins) included as fixed effects with the interaction term, and participant modeled as a random factor. The covariance structure with the lowest Bayesian  Information Criterion was used to interpret the models [26], and a false discovery rate adjustment was used to control for multiple p-wise comparisons (adj) adj.

To examine the extent to which subjective ratings predicted an erse event in the next 15 minutes (main aim), adverse out es (emergency braking, lane deviation, JDS ≥2.7 and JDS 4.5+, and EEG-microsleep events) were dichotomized (occurring vs. t occurring in each 15-minute block) and were subject to a binary logistic regression with receiver operating characteristic  (ROC) Curve analysis, with the subjective rating as a predictor and dic atomized adverse event as the outcome. Based on a previous s y with similar lar methodology [28] (n = 9/1800 data points), we r are 60 data points to predict lane deviations with a medium eff size (OR > 3.47). With 224 observations available for analysis (n = 16 participants × 7 15-minute time bins × 2 conditions), we had >95% power to detect a medium effect.

Your den’s J index was used to derive the optimal cutoff for each predictor in the ROC analyses, and sensitivity, specificity, and odd ratio for optimal thresholds were reported using binary logistic regression but with a dichotomous predictor (above/below the third old) and dichotomous outcome (yes/no event). Where the odd ratio was undefined (e.g. a zero in the contingency table),   we adjusted the odds ratio using the Haldane-Anscombe correction followed by Fisher's Exact Test of significance [29, 30]. Where the is uncertainty around the true accuracy of the OR (i.e. a high  OR with a wide 95% CI), we highlight only Oonly significant ORs rs Exact) and of moderate effect size (OR > 3.47) and report only the lower limit 95% CI (i.e. providing confidence in observing a medium effect size, but being cautious on interpreting the large effect size). SPSS v27 was used for all statistical analyses.

Missing data

There were eight bins of missing data across the sample due to the d e being terminated early for N = 5 participants during the postpost-shiftve. As this was due to the instructor deeming the participant t impaired to continue driving, they were not considered missing a random. For N = 1, the first 15-minute subjective assessment of t post-shift drive was not collected, and for N = 2, the seventh  15-minute bin of subjective assessment for the post-sleep drive was l  for all subjective measures. Finally, for N = 1, there were three ming bins of data for the LFA (× 2), and SSQ4 (× 1).

Results

Sixteen shift workers (age 18–65 years) were recruited and completed both the post-sleep and post-night-shift driving conditions. The night shift was, on average (± SD), 8.3 (± 4.1 hours) with at st 5 hours occurring between 22:00 and 08:00. Following the nig shift, and relative to the post-sleep condition, participants had ss sleep prior before-rive (0.4 [± 1.1 hours] vs. 7.6 [± 2.4   hours]) and had been awake for longer (12.8 [± 4.8 hours] vs. 5.0  [± 1.7 hours]). The time of day was controlled and largely comparable between the two drives (average 1.0 [± 1.2 hours] time difference between the drives). Relative to the post-sleep drive,   the post-night-shift drive was associated with more lane deviations (105 vs. 117, p < 0.0001) and severe driving events (0% versus 37.5% of all drives, p < 0.01), as described previously [18]. Relative t riving post-sleep, the night shift also resulted in an increased ner of JDS severe (0.19 vs. 1.14 per 15 minutes, p = 0.003) and mod the impairment scores (1.17 vs. 3.1 per 15 minutes, p = 0.006). There were also more “end state” microsleep events (0.05   vs. 0.21 per 15 minutes [total, 5 vs. 23], p < 0.001). The extent to which drivers were able to identify sleepiness and how subjective ratings of sleepiness predicted these moderate and severe adv e driving outcomes form the basis of this study.

Subjective sleepiness while driving post-night shift.

Similar changes were evident in self-reported sleepiness and sle ness symptoms. Compared to driving post-sleep, drivers post-night-shift reported higher KSS (3.4 vs. 5.8, p < 0.001);   increased LFA in the next 5 minutes (3.2 vs. 4.4, p < 0.001);  increased frequency of sleepiness symptoms (14.0 vs. 25.7, p = 0.001). See Figure 1A–C. These symptoms included struggling to p the eyes open (p < 0.001), vision becoming blurred (p = 0.007), nodding off to sleep (p = 0.001), difficulty keeping to the mid of the road (p < 0.001), difficulty maintaining the correct speed (p < 0.001), mind wandering to other things (p < 0.001), responses were slowing (p< 0.001), and the head dropping down (p = 0.032). See Supplementary Figure S1. The main eThe mains of dri time were also observed for all subjective variables (p < 0.001, see Figure 1D–F), including individual sleepiness symptoms (p < 0.004, see Supplementary Figure S1), such that the frequency of all sleepiness symptoms increased as a function of drive time. No shift × drive time interactions were observed  (p > 0.079), except for difficulty keeping to the middle of the road p = 0.041) and mind wandering (p = 0.008); each becoming more frequent with drive time for the post-night-shift drive  (see Supplementary Figure S1).

Subjective sleepiness and prediction of drowsy driving events.

Drives were examined in 15-minute bins, with each bin dichotomized concerning a drowsy driving event that occurred o ot. As seen in Figure 2, 11 bins were positive for a severe driving event (post-shift = 10 bins), 71 were positive for a moderate  (lane deviation) event (post-shift = 40 bins), 74 bins were positive for severe (JDS 4.5+) drowsiness event (post-shift = 43 bins), 76   for a moderate (JDS 2.7+) drowsiness event (post-shift = 47 bins),   and 28 bins were positive for a microsleep event (post-shift = 23   bins). For the post-shift drive, seven participants (44%) had at l t one severe (near-crash) event, while 12 participants (75%)   exhibited a moderate (lane deviation) driving event. For physiological outcomes, 13 participants (81%) had at least one moderate drowsiness event (JDS 2.7+), 6 (38%) had at least one severe d business event (JDS 4.5+), and 5 (31%) experienced a microsleep. Subsequent analyses sought to examine the extent to which s active sleepiness predicted the occurrence of an objective s princess event in the next 15 minutes (i.e. the subsequent bin).

Severe driving events (emergency braking).

No severe driving event occurred without noticeable symptoms o sleepiness beforehand. In the 15 minutes before severe ding events, drivers reported a KSS of 7 or greater and a possibility of falling asleep in the next few minutes (50% reported that this was likely or very likely). At least one sleepiness symptom was reported prior before severe driving events: 90% of severe driving events were preceded by reports of struggling to keep the e  open and mind wandering, 80% involved reports of the vision bec ng blurry and responses slowing, while 70% involved a   feeling of nodding off to sleep, difficulty keeping to the middle of the road or maintaining the correct speed. Only 30% of severe   driving events involved reports of the head dropping in the prior  15 minutes

For each point increase in KSS or LFA, there was a respective  2.4 (p = 0.016) and 2.1 (p = 0.009) increased odds of severe driving it occurring in the next 15 minutes. See Table 2 and Figure 3. All (SSQ) sleepiness symptoms were associated with increasing odds of a severe event occurring in the next 15 minutes, with excepted dropping Each one-point increase in the SSQ scale for struggling to keep the eyes open, vision becoming blud, difficulty keeping to the center of the road, and mind waning was associated with more than a 2-fold increase in the ds of an impending severe driving event. See Table 2 and  Figure 3.

Using ROC analyses, ocular-related sleepiness symptoms  (struggling to keep eyes open and vision becoming blurred) were the strongest predictors of having a severe driving event in the next 15 minutes (AUC 0.91, p ≤ 0.001, for both). The KSS, LFA, and the sleepiness symptoms of difficulty maintaining l  position, responses being slower, mind wandering to other t gs, and being aware of having fallen asleep, were also strong predictors of a severe driving event (AUC > 0.85, p < 0.004). In crust, noticing the head dropping was a poor predictor of a   severe driving event (p = 0.21). See Table 2 and Figure 4.

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